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DBMS > Apache Phoenix vs. Citus vs. Databricks vs. MarkLogic

System Properties Comparison Apache Phoenix vs. Citus vs. Databricks vs. MarkLogic

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonCitus  Xexclude from comparisonDatabricks  Xexclude from comparisonMarkLogic  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Operational and transactional Enterprise NoSQL database
Primary database modelRelational DBMSRelational DBMSDocument store
Relational DBMS
Document store
Native XML DBMS
RDF store infoas of version 7
Search engine
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Websitephoenix.apache.orgwww.citusdata.comwww.databricks.comwww.progress.com/­marklogic
Technical documentationphoenix.apache.orgdocs.citusdata.comdocs.databricks.comwww.progress.com/­marklogic/­documentation
DeveloperApache Software FoundationDatabricksMarkLogic Corp.
Initial release2014201020132001
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20198.1, December 201811.0, December 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL, commercial license also availablecommercialcommercial inforestricted free version is available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCC++
Server operating systemsLinux
Unix
Windows
LinuxhostedLinux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesFlexible Schema (defined schema, partial schema, schema free)schema-free infoSchema can be enforced
Typing infopredefined data types such as float or dateyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.noyes infospecific XML type available, but no XML query functionalityyesyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesyes infostandard, with numerous extensionswith Databricks SQLyes infoSQL92
APIs and other access methodsJDBCADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Python
R
Scala
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functionsuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions and aggregatesyes infovia XQuery or JavaScript
Triggersnoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replication infoother methods possible by using 3rd party extensionsyesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDACID infocan act as a resource manager in an XA/JTA transaction
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonoyes, with Range Indexes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyfine grained access rights according to SQL-standardRole-based access control at the document and subdocument levels
More information provided by the system vendor
Apache PhoenixCitusDatabricksMarkLogic
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache PhoenixCitusDatabricksMarkLogic
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